Magnetic Resonance Imaging (MRI) apparatuses are configured so as to magnetically excite nuclear spins of a subject placed in a static magnetic field by using Radio Frequency (RF) signals at a Larmor frequency and to reconstruct a Magnetic Resonance (MR) image from Magnetic Resonance (MR) signals generated due to the excitation. Examples of imaging methods that use such MRI apparatuses include a method called “parallel imaging method”.
According to the parallel imaging method, an array coil that includes a plurality of RF coils called element coils is used. In addition, by skipping some of phase encoding processes, MR signals are collected with a reduced number of phase encoding processes that has been reduced to a value expressed as “a predetermined number of phase encoding processes required to reconstruct the MR image” divided by “the number of RE coils”. As a result, the RE coils simultaneously receive the MR signals, so that an MR image is reconstructed from the received MR signal for each of the RF coils. When such a parallel imaging method is used, the Field Of View (FOV) of the MR image that is generated for each of the RF coils is small, and it is therefore possible to shorten the scanning time period and to speed up the image taking process.
When the parallel imaging method is used, aliasing occurs at the edges of the MR images reconstructed from the MR signals that have been collected by the RF coils. For this reason, according to the parallel imaging method, by making use of the fact that the sensitivity levels of the plurality of RF coils are different from one another, an unfolding process to unfold the aliasing that has occurred in each of the plurality of MR images obtained by the RF coils is performed as a post-processing process. To perform the unfolding process, sensitivity distribution data indicating spatial sensitivity distributions of the RF coils is used. Further, a plurality of unfolded images that have been obtained as a result of the unfolding process are combined into a final FOV image. As explained here, according to the parallel imaging method, it is possible to speed up the image taking process, and it is also possible to obtain an image having a large field of view such as an image of the entire abdomen.
According to the conventional parallel imaging method described above, however, there are situations where, as explained below, the unfolded image obtained as a result of the unfolding process includes one or more pixels of which the pixel values are abnormal and where the quality of the unfolded image is degraded by such pixels.
As described above, according to the conventional parallel imaging method, by making use of the difference in the sensitivity distributions between the element coils included in the array coil, the unfolding process is performed, based on the MR images that are generated in correspondence with the receiving channels and in which the aliasing has occurred and based on the sensitivity distribution data of the channels. During the unfolding process, pixel values are calculated with respect to different points in the image obtained as a result of the unfolding process by using, for example, Expression (1) shown below, so that the unfolded image in which the aliasing is unfolded is generated by putting together the calculated pixel values at the different points.x=(SHS)−1SHy  (1)
In Expression (1), “x” denotes the signal intensities at the points in the unfolded image (i.e., vectors of which the quantity is equal to N[pt]), whereas “y” denotes the intensities of the signals measured by the element coils (i.e., vectors of which the quantity is equal to N[ch]: y={V(1), V(2), . . . V(N[ch])}). Further, “S” denotes the coil sensitivity levels at the points being the target of the unfolding process (i.e., an N[CH]×N[pt] matrix), whereas “SH” denotes a transposed conjugate matrix.
During such an unfolding process, generally speaking, a pseudo inversed matrix is calculated by performing a lower-upper (LU) decomposition process. In a division process performed during the LU decomposition, there are situations where the divisor is extremely small. This phenomenon occurs, for example, when the sensitivity difference between the channels in the spatial positions in the sensitivity distribution data used in the unfolding process is small or when the subject mistakenly moved between a scanning process performed to create the sensitivity distribution data and a scanning process in a main image-taking process.
In the case where the divisor in the LU decomposition is extremely small as mentioned above, the components of the inverse matrix will have extremely large values. As a result, there are situations in which the absolute values of the pixels values of some of the pixels in the unfolded image resulting from the unfolding process are extremely large. FIG. 8 is a drawing for explaining the problems with the conventional technique. As shown in FIG. 8, there are situations in which an unfolded image 31 includes pixels 32 of which the pixel values are extremely large, in such positions that are, for example, near an edge portion of the image. As a result, there are situations where the quality of the unfolded image is degraded by such pixels of which the pixel values are abnormal.